3 ms·

Great post. This is exactly what I'm an expert at, I even coined a term in the field [1], :). Since I started doing this 15 years ago (and I know the field pr

Great post. This is exactly what I'm an expert at, I even coined a term in the field [1], :). Since I started doing this 15 years ago (and I know the field predates me by much), one always has had this feeling that we are so close to a big breakthrough in biological simulation, but at the same time, progress has been kind of "slow". I think the reason for that is because pushing the envelope forward in this field requires mastering three (maybe four) different disciplines, your pick of [Bio, Chem, CS, Math, Physics]. Very few people reach this level of simultaneous understanding of all these pieces. I'm not trying to gatekeep the field, though, much of the progress here (including many of the papers mentioned in TFA) is work coming from PhD students. Anyone could jump into this, but you really need to sit down and try to make sense of it for a while, years. PhD gives one the perfect opportunity for that. Anyway, I hope this thing keeps going on forward, it's one of the ultimate goals of Biology and it would be extremely beneficial to the world. 1: https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2016.01881/full https://www.frontiersin.org/journals/plant-science/articles/...

_factor 1y ago
Thank you for your contributions. You are quite literally saving lives. Are there any good local (op-so ideally) tools and/or libraries one can experiment with? I have access to a couple HPC clusters and would love to learn more.
- moralestapia 1y ago
  Sure! Take a look at SimTK [1]. And I would try to reproduce Karr's model [2], paper here [3]; also mentioned in the linked page. This is the study that made me, and many others at the time, to actually take this seriously, lol. I was a student and was doing this as a hobby project, Karr's paper made me think "wait, this is actually possible, and today". It's really good if you want to learn and get your feet wet on this. If you want, you can reach out to me at hn @ moralestapia . com, and I'll be happy to recommend some more stuff! 1: https://simtk.org/ https://simtk.org/ 2: https://simtk.org/projects/wholecell https://simtk.org/projects/wholecell 3: https://www.cell.com/cell/fulltext/S0092-8674(12)00776-3 https://www.cell.com/cell/fulltext/S0092-8674(12)00776-3
ulnarkressty 1y ago
A noob question, since the original article doesn't go into details - what is exactly being simulated here? I was under the impression that we can't even reliably do a single protein folding due to the sheer complexity of the task. So how do we simulate the zillions that are bouncing around in a single cell? And if we don't simulate it at that level, how are we confident that it is correct?
- andoando 1y ago
  I assume same reason we dont need to simulate quantum physics to simulate a ball moving or even the weather.
- moralestapia 1y ago
  You're right, they're only approximations at different levels, as a 1:1 reproduction of even a single cell would be unfeasible. Most of them are built around one specific, measurable, phenotype that they want to reproduce, like estimate metabolite input/output over time. Some others attempt to model the behavior of these cells when interacting with others, like in a colony or tissue. This is quite important because most of the phenomena that enables development, healing, regeneration, etc ... are emergent processes that only make sense when you study the whole tissue. One concrete thing you can measure/simulate here is "if I drop this hormone here, where is it going to be at time X and at what concentration" [1], which is super useful to do in silico because measuring that in real tissue, without or even with markers, is much more complicated, expensive and time consuming. 1: I wrote one of the first models that was able to do this in realistic plant tissue. Realistic here means, bounded by the chemical/physical constraints found in real plants and using a structural scaffold that resembles them as well.
udara 1y ago
Thank you! and it's awesome you can contribute to the subject! You're so right that it feels so difficult to make sense of because of how cross-disciplinary it is. I hope more people invest and work on this stuff as well. I'm hoping to learn more over the years!
smj-edison 1y ago
This is exactly the field I want to enter! I really want to work on the tooling side for atomic simulation (I think I have a design that could complete each timestep in ~10usec that doesn't lose speed as it scales). I think it would be cool to automatically extract parameters for coarser grained models. I'm planning to go to college for electrical engineering (ASIC design), but swap out some of my requirements to focus on particle physics. The college I got into also has an undergraduate MD lab that I got invited to. Do you have any tips on what skills you've found most valuable as you've done simulation?
- moralestapia 1y ago
  >This is exactly the field I want to enter! Please do! >I really want to work on the tooling side for atomic simulation [...] Please do x2. That's how I started with this. With respect to simulations, become as good as you can with the methods that enable them. ODEs, PDEs and how to compute (well ... approximate) them. Spend some time making sure you understand the math properly. You don't need to spend ages here, most of the equations used in the field involve no more than three variables. The trick is in how you solve them, but solvers already exist, ofc. Write your own FEM solver, this is a must. It's not going to be SOTA, but you'll get a good feel for all the fundamentals. Then you can move on to using whichever you need/want because you'll understand what they do. (I used deal.ii a lot, but there are many more good ones out there). I would recommend you work with C/C++ instead of Python (ofc. you can do both). The reason for this is that you'll kill two birds with one stone by getting a good sense of how a computer actually executes things. Python is too abstracted away from that, and in this particular field, you really need to know what the hardware is doing and how. Then move into GPUs. Actually, if you can write this FEM solver in something like CUDA or JAX, you will kill three birds with one stone. I would then try to join a research group doing this, even if it's for free, only a couple months. My personal preferred niche is what is now called morphodynamics. Just approach any of these groups and tell them "Hey, I'm good with CUDA/JAX, I wrote this solver, I made this small simulation, I'm interested in doing biological simulations and want to learn more", 8/10 will say yes, there aren't many people out there with this skillset, it's not crowded. I mentioned "even if it's for free", the thing is, everyone says yes to free, lol. Your goal is to get one paper out there with your name on it. It doesn't have to be your own idea/project, just help them build whatever they're doing and make sure your name is there. Then ... you're pretty much in. You can stay there or go to a different group, but now you can say "hey I worked on this project, here's the paper, I can do X" and you take it from there. :D If you want to be in touch email me hn @ moralestapia . com. My profile seems empty bc. our friend @dang hid it, but just send me an email.
RivieraKid 1y ago
> progress has been kind of "slow" Isn't it simply because it's a fundamentally hard problem that may not even be solvable? Simulating a 50 amino acids long protein in water for 1 ms on a top supercomputer using molecular dynamics would take about a week. Can the current approach lead to models that are even remotely as useful as a full molecular dynamics simulation? The current approach requires us to first discover the hard stuff, the myriads of tiny mechanisms happening in the cell.
- moralestapia 1y ago
  Hmm ... a good analogy to answer your question would be. CFD exists and has been fundamental to shape the world as we know it (refer to Wiki page [1] to learn more about what it is and why it is important). CFD is also not a full molecular dynamics simulation, yet is useful. Another example could be weather models. None of them, afaik, simulate @RivieraKid typing a comment in HN, an action which, infinitesimally, affects the weather of the planet. And yet, they're still very useful. You work with approximations, some of them are good enough to give you 80% of the answers you want, and that 80% is more than enough to improve our quality of life significantly. 1: https://en.wikipedia.org/wiki/Computational_fluid_dynamics https://en.wikipedia.org/wiki/Computational_fluid_dynamics
  - RivieraKid 1y ago
    I understand the principle that models of reality don't need to be 100% accurate to be useful. But... the way MD, CFD or weather models approximate reality seems fundamentally different from how current virtual cell models do it. In MD, CFD or weather models, all of the relevant low-level mechanisms are known, that's why when we want to study a system's reaction to some external shock, we know we will get a roughly correct result with some well-defined error. But in a virtual cell many low-level interactions are not modelled and these interactions can have large effect. Often they are not modelled because they simply haven't been discovered yet. With an accurate cell model we would be able to discover all of those mechanisms, that's where most of the value is. Here's a flawed analogy: Current virtual cell models are like LLMs. They give us permutations of what we already know but they can't discover what we haven't discovered yet.

_factor 1y ago

Thank you for your contributions. You are quite literally saving lives. Are there any good local (op-so ideally) tools and/or libraries one can experiment with? I have access to a couple HPC clusters and would love to learn more.

moralestapia 1y ago
Sure! Take a look at SimTK [1]. And I would try to reproduce Karr's model [2], paper here [3]; also mentioned in the linked page. This is the study that made me, and many others at the time, to actually take this seriously, lol. I was a student and was doing this as a hobby project, Karr's paper made me think "wait, this is actually possible, and today". It's really good if you want to learn and get your feet wet on this. If you want, you can reach out to me at hn @ moralestapia . com, and I'll be happy to recommend some more stuff! 1: https://simtk.org/ https://simtk.org/ 2: https://simtk.org/projects/wholecell https://simtk.org/projects/wholecell 3: https://www.cell.com/cell/fulltext/S0092-8674(12)00776-3 https://www.cell.com/cell/fulltext/S0092-8674(12)00776-3

ulnarkressty 1y ago

A noob question, since the original article doesn't go into details - what is exactly being simulated here? I was under the impression that we can't even reliably do a single protein folding due to the sheer complexity of the task. So how do we simulate the zillions that are bouncing around in a single cell? And if we don't simulate it at that level, how are we confident that it is correct?

andoando 1y ago
I assume same reason we dont need to simulate quantum physics to simulate a ball moving or even the weather.
moralestapia 1y ago
You're right, they're only approximations at different levels, as a 1:1 reproduction of even a single cell would be unfeasible. Most of them are built around one specific, measurable, phenotype that they want to reproduce, like estimate metabolite input/output over time. Some others attempt to model the behavior of these cells when interacting with others, like in a colony or tissue. This is quite important because most of the phenomena that enables development, healing, regeneration, etc ... are emergent processes that only make sense when you study the whole tissue. One concrete thing you can measure/simulate here is "if I drop this hormone here, where is it going to be at time X and at what concentration" [1], which is super useful to do in silico because measuring that in real tissue, without or even with markers, is much more complicated, expensive and time consuming. 1: I wrote one of the first models that was able to do this in realistic plant tissue. Realistic here means, bounded by the chemical/physical constraints found in real plants and using a structural scaffold that resembles them as well.

udara 1y ago

Thank you! and it's awesome you can contribute to the subject! You're so right that it feels so difficult to make sense of because of how cross-disciplinary it is. I hope more people invest and work on this stuff as well. I'm hoping to learn more over the years!

smj-edison 1y ago

This is exactly the field I want to enter! I really want to work on the tooling side for atomic simulation (I think I have a design that could complete each timestep in ~10usec that doesn't lose speed as it scales). I think it would be cool to automatically extract parameters for coarser grained models. I'm planning to go to college for electrical engineering (ASIC design), but swap out some of my requirements to focus on particle physics. The college I got into also has an undergraduate MD lab that I got invited to. Do you have any tips on what skills you've found most valuable as you've done simulation?

moralestapia 1y ago
>This is exactly the field I want to enter! Please do! >I really want to work on the tooling side for atomic simulation [...] Please do x2. That's how I started with this. With respect to simulations, become as good as you can with the methods that enable them. ODEs, PDEs and how to compute (well ... approximate) them. Spend some time making sure you understand the math properly. You don't need to spend ages here, most of the equations used in the field involve no more than three variables. The trick is in how you solve them, but solvers already exist, ofc. Write your own FEM solver, this is a must. It's not going to be SOTA, but you'll get a good feel for all the fundamentals. Then you can move on to using whichever you need/want because you'll understand what they do. (I used deal.ii a lot, but there are many more good ones out there). I would recommend you work with C/C++ instead of Python (ofc. you can do both). The reason for this is that you'll kill two birds with one stone by getting a good sense of how a computer actually executes things. Python is too abstracted away from that, and in this particular field, you really need to know what the hardware is doing and how. Then move into GPUs. Actually, if you can write this FEM solver in something like CUDA or JAX, you will kill three birds with one stone. I would then try to join a research group doing this, even if it's for free, only a couple months. My personal preferred niche is what is now called morphodynamics. Just approach any of these groups and tell them "Hey, I'm good with CUDA/JAX, I wrote this solver, I made this small simulation, I'm interested in doing biological simulations and want to learn more", 8/10 will say yes, there aren't many people out there with this skillset, it's not crowded. I mentioned "even if it's for free", the thing is, everyone says yes to free, lol. Your goal is to get one paper out there with your name on it. It doesn't have to be your own idea/project, just help them build whatever they're doing and make sure your name is there. Then ... you're pretty much in. You can stay there or go to a different group, but now you can say "hey I worked on this project, here's the paper, I can do X" and you take it from there. :D If you want to be in touch email me hn @ moralestapia . com. My profile seems empty bc. our friend @dang hid it, but just send me an email.

RivieraKid 1y ago

> progress has been kind of "slow" Isn't it simply because it's a fundamentally hard problem that may not even be solvable? Simulating a 50 amino acids long protein in water for 1 ms on a top supercomputer using molecular dynamics would take about a week. Can the current approach lead to models that are even remotely as useful as a full molecular dynamics simulation? The current approach requires us to first discover the hard stuff, the myriads of tiny mechanisms happening in the cell.

moralestapia 1y ago
Hmm ... a good analogy to answer your question would be. CFD exists and has been fundamental to shape the world as we know it (refer to Wiki page [1] to learn more about what it is and why it is important). CFD is also not a full molecular dynamics simulation, yet is useful. Another example could be weather models. None of them, afaik, simulate @RivieraKid typing a comment in HN, an action which, infinitesimally, affects the weather of the planet. And yet, they're still very useful. You work with approximations, some of them are good enough to give you 80% of the answers you want, and that 80% is more than enough to improve our quality of life significantly. 1: https://en.wikipedia.org/wiki/Computational_fluid_dynamics https://en.wikipedia.org/wiki/Computational_fluid_dynamics
- RivieraKid 1y ago
  I understand the principle that models of reality don't need to be 100% accurate to be useful. But... the way MD, CFD or weather models approximate reality seems fundamentally different from how current virtual cell models do it. In MD, CFD or weather models, all of the relevant low-level mechanisms are known, that's why when we want to study a system's reaction to some external shock, we know we will get a roughly correct result with some well-defined error. But in a virtual cell many low-level interactions are not modelled and these interactions can have large effect. Often they are not modelled because they simply haven't been discovered yet. With an accurate cell model we would be able to discover all of those mechanisms, that's where most of the value is. Here's a flawed analogy: Current virtual cell models are like LLMs. They give us permutations of what we already know but they can't discover what we haven't discovered yet.